style: 项目重构
1.项目改名为kilostar（千星） 2.后端部分进行大规模重构 3.node功能进行大规模重新设计
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# Copyright 2026 zhaoxi826
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from .control_node import ControlNode
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__all__ = ["ControlNode"]
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# Copyright 2026 zhaoxi826
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import ray
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from pydantic_ai import Agent, RunContext
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from kilostar.core.global_state_machine.global_state_machine import GlobalStateMachine
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from kilostar.core.global_state_machine.model_provider.base_provider import Provider
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from kilostar.adapter.model_adapter.agent_factory import AgentFactory
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from kilostar.core.individual.control_node.template import (
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ForWorkflow,
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ForWorkflowInput,
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ControlNodeDeps,
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)
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@ray.remote
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class ControlNode:
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"""ControlNode 核心组件类。
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这是一个系统执行节点类，作为多智能体架构中的独立处理单元。它能够接收工作流上下文，根据内置的大模型策略进行意图理解和自主决策，从而驱动特定阶段的任务闭环。"""
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def __init__(self):
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from kilostar.utils.logger import get_logger
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self.logger = get_logger("control_node")
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self.agent: Agent | None = None
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async def create_agent(
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self,
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global_state_machine: GlobalStateMachine,
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provider_title: str,
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model_id: str,
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tools_list: list[str] = None,
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) -> None:
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"""
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create_agent方法，将agent对象装配到Control的属性内
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该方法通过provider_title从global_state_machine中获取provider对象，然后从provider对象中取出供应商形象，装配为pydantic_ai的
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Agent实例，
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并挂载到self.agent属性
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Args:
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global_state_machine: 全局状态机
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provider_title: 供应商名
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model_id: 模型id
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Returns:
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无返回
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"""
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system_prompt: str = (
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"你叫kilostar，是一个多智能体AI助手系统中的【控制节点 (Control Node)】。\n"
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"你是系统的'执行者'和'车间主任'，专门负责执行工作流中分配给你的具体子任务。\n"
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"你的工作职责是：\n"
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"1. 仔细分析分配给你的工作流步骤 (workflow_step) 的目标和要求。\n"
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"2. 运用你被分配的工具 (如有) 或者依靠自身的知识和推理能力，精准、高效地完成该任务。\n"
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"3. 将执行的结果、产生的数据或者具体的输出，严格按照 ForWorkflow 格式返回。\n"
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"请注意：你的输出应当具体、实用，直接提供任务所要求的结果，不要做过多无关的寒暄。"
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)
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output_type = ForWorkflow
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from kilostar.utils.get_tool import load_tools_from_list
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provider: Provider = await global_state_machine.get_provider.remote(
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provider_title
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)
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agent_factory = AgentFactory()
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callables = load_tools_from_list(tools_list)
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self.agent = agent_factory.create_agent(
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provider=provider,
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model_id=model_id,
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output_type=output_type,
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system_prompt=system_prompt,
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deps_type=ControlNodeDeps,
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agent_name="control_node",
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tools=callables,
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)
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@self.agent.system_prompt
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async def dynamic_prompt(ctx: RunContext[ControlNodeDeps]):
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"""执行与 dynamic prompt 相关的核心业务流转操作。
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该方法封装了具体的算法策略或状态控制逻辑，确保操作能够在事务上下文中被原子且一致地执行。
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Args: ctx (RunContext[ControlNodeDeps]): 参与 dynamic prompt 逻辑运算或数据构建的上下文依赖对象。
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Returns: : 经由当前业务模型加工处理后所输出的具体数据实例或领域模型对象。"""
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prompt = system_prompt + "\n\n"
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prompt += (
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f"=== 当前任务步骤上下文 ===\n"
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f"- 步骤名称 (Name): {ctx.deps.workflow_step.name}\n"
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f"- 步骤目标/描述 (Description): {ctx.deps.workflow_step.desc}\n"
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f"- 前置输入（input）: {ctx.deps.workflow_step.inputs}\n"
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)
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return prompt
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async def working(self, payload: ForWorkflowInput) -> str:
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"""执行与 working 相关的核心业务流转操作。
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该方法封装了具体的算法策略或状态控制逻辑，确保操作能够在事务上下文中被原子且一致地执行。
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Args: payload (ForWorkflowInput): 从客户端传递过来或由上游组件生成的核心业务数据体，通常需要进一步的清洗和结构化解析。
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Returns: (str): 处理流程所输出的具体字符串产物，可能是新生成的 ID 序列、格式化好的文本片段或 LLM 推理的回答内容。"""
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try:
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result: ForWorkflow = await self._run(payload)
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return result
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except Exception:
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self.logger.exception("ControlNode在执行working时发生严重错误")
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return None
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async def _run(self, payload: ForWorkflowInput) -> ForWorkflow:
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"""执行与 run 相关的核心业务流转操作。
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该方法封装了具体的算法策略或状态控制逻辑，确保操作能够在事务上下文中被原子且一致地执行。
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Args: payload (ForWorkflowInput): 从客户端传递过来或由上游组件生成的核心业务数据体，通常需要进一步的清洗和结构化解析。
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Returns: (ForWorkflow): 经由当前业务模型加工处理后所输出的具体数据实例或领域模型对象。"""
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try:
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self.agent.retries = 3
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deps = ControlNodeDeps(workflow_step=payload.workflow_step)
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self.logger.debug(
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f"ControlNode: 开始执行工作流节点 [{payload.workflow_step.name}] (原生重试开启)"
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)
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result = await self.agent.run(
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f"请根据提供的 workflow_step 上下文，执行此步骤并输出结果。\n详细指令或附加数据：{payload.workflow_step.model_dump_json()}",
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deps=deps,
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)
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return result.output
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except Exception as e:
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self.logger.exception(
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f"ControlNode 在执行步骤 [{payload.workflow_step.name}] 时最终失败: {str(e)}"
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)
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raise RuntimeError(f"ControlNode 执行步骤失败: {str(e)}") from e
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@@ -0,0 +1,55 @@
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# Copyright 2026 zhaoxi826
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from pydantic import Field
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from kilostar.core.workflow_running_engine.workflow import WorkStep
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from kilostar.utils.agent_model import ResponseModel, InputModel, DepsModel
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class ControlNodeResponse(ResponseModel):
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"""控制节点回复的基类"""
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pass
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class ControlNodeInput(InputModel):
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"""ControlNodeInput 核心组件类。
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这是一个系统执行节点类，作为多智能体架构中的独立处理单元。它能够接收工作流上下文，根据内置的大模型策略进行意图理解和自主决策，从而驱动特定阶段的任务闭环。"""
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pass
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class ControlNodeDeps(DepsModel):
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"""ControlNodeDeps 核心组件类。
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这是一个系统执行节点类，作为多智能体架构中的独立处理单元。它能够接收工作流上下文，根据内置的大模型策略进行意图理解和自主决策，从而驱动特定阶段的任务闭环。"""
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workflow_step: WorkStep
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# In the future, this can be dynamically populated with tools specific to the current task execution
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class ForWorkflow(ControlNodeResponse):
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"""ForWorkflow 核心组件类。
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这是一个领域数据模型或功能封装类，承载了 ForWorkflow 相关的内聚属性定义与状态维护。它的存在隔离了局部的业务复杂性，并对外提供了类型安全的访问接口。"""
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output: str = Field(
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..., description="控制节点执行特定工作流步骤的结果。包含执行细节和输出数据。"
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)
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class ForWorkflowInput(ControlNodeInput):
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"""ForWorkflowInput 核心组件类。
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这是一个领域数据模型或功能封装类，承载了 ForWorkflowInput 相关的内聚属性定义与状态维护。它的存在隔离了局部的业务复杂性，并对外提供了类型安全的访问接口。"""
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workflow_step: WorkStep
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